


import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort



class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		'ssube/stable-diffusion-x4-upscaler-onnx'

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  str=0       ):
        a__	:  List[str]     =   floats_tensor((1, 3, 128, 128)				,				rng=random.Random(lowerCamelCase__       )       )
        a__	:  Optional[int]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Dict     =   {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Optional[Any]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,				provider="CPUExecutionProvider"       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[Any]     =   self.get_dummy_inputs()
        a__	:  Any     =   pipe(**lowerCamelCase__       ).images
        a__	:  Dict     =   image[0, -3:, -3:, -1].flatten()

        # started as 128, should now be 512
        assert image.shape == (1, 512, 512, 3)
        a__	:  Union[str, Any]     =   np.array(
            [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]       )
        assert np.abs(image_slice - expected_slice       ).max() < 1E-1

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  Optional[Any]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,				provider="CPUExecutionProvider"       )
        a__	:  List[Any]     =   PNDMScheduler.from_config(pipe.scheduler.config				,				skip_prk_steps=lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  List[Any]     =   self.get_dummy_inputs()
        a__	:  Optional[Any]     =   pipe(**lowerCamelCase__       ).images
        a__	:  List[str]     =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        a__	:  Tuple     =   np.array(
            [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]       )
        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  List[Any]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,				provider="CPUExecutionProvider"       )
        a__	:  List[str]     =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  List[Any]     =   self.get_dummy_inputs()
        a__	:  Dict     =   pipe(**lowerCamelCase__       ).images
        a__	:  str     =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        a__	:  Optional[int]     =   np.array(
            [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Optional[Any]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,				provider="CPUExecutionProvider"       )
        a__	:  Union[str, Any]     =   EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Any     =   self.get_dummy_inputs()
        a__	:  List[Any]     =   pipe(**lowerCamelCase__       ).images
        a__	:  List[Any]     =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        a__	:  Optional[int]     =   np.array(
            [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]       )
        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  List[str]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,				provider="CPUExecutionProvider"       )
        a__	:  Any     =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   self.get_dummy_inputs()
        a__	:  int     =   pipe(**lowerCamelCase__       ).images
        a__	:  Optional[Any]     =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        a__	:  Optional[int]     =   np.array(
            [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1




@nightly
@require_onnxruntime
@require_torch_gpu
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  @property
  def    _UpperCamelCase( self			:  Any       ):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

  @property
  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Any     =   ort.SessionOptions()
        a__	:  Tuple     =   False
        return options

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[Any]     =   load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/img2img/sketch-mountains-input.jpg"       )
        a__	:  int     =   init_image.resize((128, 128)       )
        # using the PNDM scheduler by default
        a__	:  Optional[int]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Any     =   "A fantasy landscape, trending on artstation"

        a__	:  Dict     =   torch.manual_seed(0       )
        a__	:  Tuple     =   pipe(
            prompt=lowerCamelCase__				,				image=lowerCamelCase__				,				guidance_scale=7.5				,				num_inference_steps=10				,				generator=lowerCamelCase__				,				output_type="np"				,				)
        a__	:  Any     =   output.images
        a__	:  Any     =   images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        a__	:  List[str]     =   np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]       )
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2E-2

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Optional[Any]     =   load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/img2img/sketch-mountains-input.jpg"       )
        a__	:  Optional[Any]     =   init_image.resize((128, 128)       )
        a__	:  Optional[int]     =   LMSDiscreteScheduler.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"				,				subfolder="scheduler"       )
        a__	:  Optional[int]     =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"				,				scheduler=lowerCamelCase__				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   "A fantasy landscape, trending on artstation"

        a__	:  Optional[int]     =   torch.manual_seed(0       )
        a__	:  Tuple     =   pipe(
            prompt=lowerCamelCase__				,				image=lowerCamelCase__				,				guidance_scale=7.5				,				num_inference_steps=20				,				generator=lowerCamelCase__				,				output_type="np"				,				)
        a__	:  Dict     =   output.images
        a__	:  Tuple     =   images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        a__	:  Tuple     =   np.array(
            [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]       )
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2E-2









"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"